Abstract
Optimization problems are ubiquitous in scientific research, engineering as well as daily lives. However, solving a complex optimization problem often requires excessive computing resource and time, and faces challenges in easily getting trapped into local optima. Here, we propose a memristive optimizer hardware based on Hopfield network. A single memristor crossbar is used to store the weight parameters of fully-connected Hopfield network and adjust the network dynamics in situ. Furthermore, we harness the intrinsic nonlinearity of memristors within the crossbar to implement an efficient and simplified annealing process for the optimization. Solutions of continuous function optimizations as well as combinatorial are experimentally demonstrated, indicating great potential of the transiently chaotic memristive network in solving optimization problems in general.
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